NS NSF Convergence Accelerator Chaitan Baru Senior Science - - PowerPoint PPT Presentation

NS NSF Convergence Accelerator

Chaitan Baru

Senior Science Advisor, Convergence Accelerator Office Office of the Director, NSF (on assignment from SDSC, UC San Diego)

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NS NSF Bi Big Ideas

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Con Convergence Research

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The grand challenges of today will NOT be solved by one discipline working alone. They require

convergence:

the merging of ideas, approaches and technologies from widely diverse fields of knowledge to stimulate innovation and discovery.

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WHAT: A new organizational structure to accelerate the transition of convergence research into practice, in areas of national importance Characteristics

§ Use-inspired research § Testbeds, tools, living labs… § Larger, national scale § Requires partnerships with industry § Clear goals, milestones, directed deliverables

Management

§ Time-limited “tracks” § Teams and Cohorts § Cooperation and Competition § More directed management § Mission-driven evaluation

Convergence Accelerator

WHY: Leverage the science across all fields of NSF research to produce

• utcomes in an accelerated timeframe, with streamlined operations

allowing for nimbleness to support the most innovative results

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Con Conver ergen ence e Ac Accel eler erator

• r Pilot
• t Tracks

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Track A1

Goal: Enhancing scientific data discovery and use Track: Open Knowledge Networks Big Idea: Harnessing the Data Revolution

Track B1

Goal: Connecting, retraining and reskilling for jobs using AI Track: AI & Future Jobs Big Idea: Future of Work at the Human Technology Frontier

Track B2

Goal: Building STEM talent in a changing workplace Track: National Talent Ecosystem Big Idea: Future of Work at the Human Technology Frontier Vertical: Challenges specific to different topical domains such as geosciences, education, smart health, finance, and manufacturing. Horizontal: Challenges that apply to all domains, such as developing the underlying representation of facts or developing secured access capabilities.

Open Knowledge Network

Others

National Talent Ecosystem AI & Future Jobs

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Ac Accelerator “Track A1 A1”:

HA HARNE NESSING NG THE HE DATA REVOL OLUTION ON

• Advanced science data infrastructure that is

interoperable and has an open architecture (makes it easier to access and link heterogeneous data products)

• Open Knowledge Network – an open semantic

information infrastructure to discover new knowledge from multiple disparate knowledge sources

• Create a nonproprietary shared knowledge

infrastructure, with a particular focus on publicly available U.S. Government and similar public

• datasets. Challenges include underlying

representation of facts, services that perform reasoning tasks, and secured access. Domains include geosciences, education, smart health, and manufacturing.

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Ac Accelerator “Tracks B1 and B2”:

FU FUTU TURE OF F WORK AT T TH THE HUMAN-TE TECHNOLOGY FR FRONTI TIER

• AI and Future Jobs. The AI and Future of Jobs track will support

the development of mechanisms that connect workers with jobs of the future, reflecting the need for re-skilling and lifelong learning, such as predictive artificial intelligence tools, economic and labor market analyses of needed skills for future workplaces, and educational technologies needed for adult learning. Ensuring fair and ethical treatment of workers will be a key principle for this

• effort. Projects may be focused on particular industries or regions,

specific populations such as veterans, or particular workplace types such as small businesses, manufacturing, or K-12 schools.

• National Talent Ecosystem. Innovative approaches for employers to

support workers seeking the skills required for 21st century work related to data science, predictive analytics, AI/machine learning, and other technologies of the future. Successful projects will prototype innovative approaches, such as learning environments, simulations and tools for analysis or assessment, and vehicles for recruitment and engagement, with the potential for wider implementation by industry, educational institutions, and other stakeholders engaging in the co-creation of a national talent ecosystem.

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2019 2019 Con

• nvergence Accelerator
• r Pilot
• t Awards

1 1 1 1 1 1 5 3 2 2 2 2 2 3 3 3 3 5 1 1

DC

19 States plus District of Columbia

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43 Awards 21 Track A 22 Track B

9 Open Knowledge Network (21 projects)

Tr Track A1 - Cl Clusters

A7143 Information Credibility A6940 Knowledge Application Development Environment A6731 Web Data Extraction/Integration A7136 Federated Search A6677 Spatial Data Models/Methods A7908 Spatial Decision Support

Vertical Projects

A6884 Mobility A6950 Ocean Resources A7153 Finance A7099 Urban Flooding A7115 Civil Infrastructure A7152 Space Sciences A7137 Energy Systems A7123 Court Records A7095 Census A7033 Public Policy Data A7017 Molecular Data A7160 Precision Medicine A7134 Intelligent Textbooks 7043 Design & Manufacturing

Horizontal Projects

A7165 Internet Structure & Security

Projects should

• Seek “track integration”;
• Collaborate with industry;
• Encouraged to collaborate/link with other relevant efforts in the community

Graphics Designed By 30000008981 From <a href="https://lovepik.com/image-400862045/blue-circle-neon-box-lamp.html">LovePik.com</a>

Prospective Employee Prospective Employer Education/Training

Worker-Work Matching

Existing Qualifications: Education Skills Certificates Existing needs: Positions Skill requirements Locations Future needs: Emerging jobs Growth projections Market demands

Curricula and Skills Training Development Workforce Training and Education Recommendations

Track B1/B2 - Clusters

B6894 – Upskilling/reskilling for digital technologies B7010 – Assessment/Prediction/Learning – smart sensing/mixed reality B7118 – Connects data exchanges at state level B7068 – Documents competencies at the national level B6992 – AI-enabled assessment + training plan for displaced miners B6656 – Design based research + analytics identifies skill gaps and designs training B7037 – AI-driven skill gap diagnostics + recommendation engine for manufacturing B7053 – Advanced robotics for training next gen emergency responders B7833 – Deep learning predicts future jobs + training for hospitality industry B6956 – AI-driven tool for career management in STEM fields B6968 – Machine learning based tools for gig economy workers B6857 – AI-based job matching – veterans, disabled workers B7036 – Low cost AR training content development platform for SMEs B6997 – Training platform for autonomous systems B7061 – Develops ROI measurement for training programs for policymakers B6947 – National microcredential system B7063 – Microcredential system for industrial robotics technicians B7888 – Fostering a diverse AI workforce B6915 – Deep learning predicts future jobs + training for manufacturing B6970 – AI+AR platform for autism spectrum disorder workers B7019 – Cloud-based platform trains for future jobs in architecture, construction B7026 - Machine learning-based national labor market information tools

Ti Timel eline ne – Ph Phas ase e 1 an and the e Future

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2019 Pilot Cohort

Pitch

Competition

Projects Start! Phase 1

Proposals

Innovation Curriculum Accelerator DCL issued Mar 2019 Jun Sep Dec Mar 2020 Jun Sep Dec Jun 2021 Jun 2022

Phase 2: Creating Deliverables

Deliverables Projects Start Year 2 Decision 2020 Topic Workshops RFI: 75 responses submitted Pitch Comp Projects Start Phase 1 Proposals Innovation Curriculum

2020 Cohort: new tracks

2020 Solicitation RCOs Projects Start Year 2 Decision RCOs

Phase 1: Team formation, res. plan dev

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Pr Program Structure: Phase I – Pl Plan anning

• September 2019 – May 2020 (March 2020)
• Upto $1M for ~9 months, for planning, team formation, participating

in meetings and Convergence Accelerator curriculum

• CA Curriculum
• User-centered design. Provided by IDEO.
• Team Science
• Domain-specific interactions with potential collaborators
• Teams are assigned a coach from a team of coaches
• Can meet with any of the other coaches, if they wish.

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Ph Phas ase e I – Pl Plan anning…

• Monthly meetings with the full cohort (43 teams x 3 per team)
• September 2019: Webinar
• October 2019:

Kickoff in DC. Interaction with government agencies.

• November 2019: Webinar
• December 2019: Face-to-face in San Francisco. Interact with industry.
• January 2020:

Webinar

• February 2020:

Face-to-face in San Francisco. Interact with foundations, VCs

• March/April 2020: Submit Phase II proposal
• April/May 2020:

Make a “pitch” to a group from NSF, other potential funders, Foundations, VCs, …

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Ph Phas ase e II – Im Imple lementatio ion

• June 2020-May 2022. Upto $5m ($3M + $2M)